Using of Particle Swarm for Performance Optimization of Helicopter Rotor Blades
Author(s)
Giorgio Guglieri*
Affiliation(s)
Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Corso Duca degli Abruzzi, Torino, Italy.
Dipartimento di Ingegneria Meccanica e Aerospaziale, Politecnico di Torino, Corso Duca degli Abruzzi, Torino, Italy.
ABSTRACT
As part of a research activity at Politecnico di Torino, aiming to develop multi-disciplinary design procedures implementing nature inspired meta-heuristic algorithms, a performance design optimization procedure for helicopter rotors has been developed and tested. The procedure optimizes the aerodynamic performance of blades by selecting the point of taper initiation, the root chord, the taper ratio, and the maximum twist which minimize horsepower for different flight regimes. Satisfactory aerodynamic performance is defined by the requirements which must hold for any flight condition: the required power must be minimized, both the section drag divergence Mach number on the advancing side of the rotor disc and the maximum section lift coefficient on the retreating side of the rotor disc must be avoided and, even more important, the rotor must be trimmed. The procedure uses a comprehensive mathematical model to estimate the trim states of the helicopter and the optimization algorithm consists of a repulsive particle swarm optimization program. A comparison with an evolutionary micro-genetic algorithm is also presented.
As part of a research activity at Politecnico di Torino, aiming to develop multi-disciplinary design procedures implementing nature inspired meta-heuristic algorithms, a performance design optimization procedure for helicopter rotors has been developed and tested. The procedure optimizes the aerodynamic performance of blades by selecting the point of taper initiation, the root chord, the taper ratio, and the maximum twist which minimize horsepower for different flight regimes. Satisfactory aerodynamic performance is defined by the requirements which must hold for any flight condition: the required power must be minimized, both the section drag divergence Mach number on the advancing side of the rotor disc and the maximum section lift coefficient on the retreating side of the rotor disc must be avoided and, even more important, the rotor must be trimmed. The procedure uses a comprehensive mathematical model to estimate the trim states of the helicopter and the optimization algorithm consists of a repulsive particle swarm optimization program. A comparison with an evolutionary micro-genetic algorithm is also presented.
KEYWORDS
Helicopter Flight Mechanics; Nature Inspired Meta-Heuristic Algorithms; Optimal Design Methods
Helicopter Flight Mechanics; Nature Inspired Meta-Heuristic Algorithms; Optimal Design Methods
Cite this paper
G. Guglieri, "Using of Particle Swarm for Performance Optimization of Helicopter Rotor Blades," Applied Mathematics, Vol. 3 No. 10, 2012, pp. 1403-1408. doi: 10.4236/am.2012.330197.
G. Guglieri, "Using of Particle Swarm for Performance Optimization of Helicopter Rotor Blades," Applied Mathematics, Vol. 3 No. 10, 2012, pp. 1403-1408. doi: 10.4236/am.2012.330197.
References
[1] R. Celi, “Recent Applications of Design Optimization to Rotorcraft—A Survey,” American Helicopter Society 55th Annual Forum, Montreal, 25-27 May 1999. [2] R. Ganguli, “Survey of Recent Developments in Rotorcraft Design Optimization,” Journal of Aircraft, Vol. 41, No. 3, 2004, pp. 493-510. doi:10.2514/1.58 [3] J. L. Walsh, “Performance Optimization of Helicopter Rotor Blades,” NASA Langley Research Center, Hampton, USA, 1991. [4] D. E. Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning,” Addison-Wesley, Reading, 1989. [5] X.-S. Yang, “Nature-Inspired Meta-Heuristic Algorithms: Second Edition,” Luniver Press, Frome, 2010. [6] R. C. Eberhart and J. Kennedy, “Particle Swarm Optimization,” Proceedings of the 4th IEEE International Conference on Neural Networks, Piscataway, 27 November-1 December 1995, pp. 1942-1948. [7] S. K. Mishra, “Global Optimization by Particle Swarm Method: A Fortran Program,” 2007. http://mpra.ub.uni-muenchen.de/874 [8] G. Guglieri and R. Celi, “On Some Aspects of Helicopter Flight Dynamics in Steady Turns,” Journal of Guidance, Control and Dynamics, Vol. 21, No. 3, 1998, pp. 383-390. doi:10.2514/2.4270 [9] T. S. Beddoes, “Representation of Airfoil Behaviour,” Vertica, Vol. 7, No. 2, 1983, pp. 183-197. [10] D. A. Peters and N. Haquang, “Dynamic Inflow for Practical Applications,” Journal of the American Helicopter Society, Vol. 33, No. 4, 1988, pp. 64-68. doi:10.4050/JAHS.33.64 [11] R. Celi, “Hingeless Rotor Dynamics in Coordinated Turns,” Journal of the American Helicopter Society, Vol. 36, No. 4, 1991, pp. 39-47. doi:10.4050/JAHS.36.39 [12] D. L. Carrol, “Chemical Laser Modeling with Genetic Algorithms,” AIAA Journal, Vol. 34, No. 2, 1996, pp. 338-346. doi:10.2514/3.13069 [13] D. L. Carroll, “Genetic Algorithms and Optimizing Chemical Oxygen-Iodine Lasers,” Developments in Theoretical and Applied Mechanics, Vol. 18, 1996, pp. 411-424. [14] R. Fantinutto, G. Guglieri and F. Quagliotti, “Flight Control System Design and Optimisation with a Genetic Algorithm,” Aerospace Science and Technology, Vol. 9, No. 1, 2005, pp. 73-80. doi:10.1016/j.ast.2004.09.003 [15] G. Guglieri, B. Pralio and F. Quagliotti, “Flight Control System Design for a Micro Aerial Vehicle,” Aircraft Engineering and Aerospace Technology, Vol. 78, No. 2, 2006, pp. 87-97. doi:10.1108/17488840610653397
[1] R. Celi, “Recent Applications of Design Optimization to Rotorcraft—A Survey,” American Helicopter Society 55th Annual Forum, Montreal, 25-27 May 1999. [2] R. Ganguli, “Survey of Recent Developments in Rotorcraft Design Optimization,” Journal of Aircraft, Vol. 41, No. 3, 2004, pp. 493-510. doi:10.2514/1.58 [3] J. L. Walsh, “Performance Optimization of Helicopter Rotor Blades,” NASA Langley Research Center, Hampton, USA, 1991. [4] D. E. Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning,” Addison-Wesley, Reading, 1989. [5] X.-S. Yang, “Nature-Inspired Meta-Heuristic Algorithms: Second Edition,” Luniver Press, Frome, 2010. [6] R. C. Eberhart and J. Kennedy, “Particle Swarm Optimization,” Proceedings of the 4th IEEE International Conference on Neural Networks, Piscataway, 27 November-1 December 1995, pp. 1942-1948. [7] S. K. Mishra, “Global Optimization by Particle Swarm Method: A Fortran Program,” 2007. http://mpra.ub.uni-muenchen.de/874 [8] G. Guglieri and R. Celi, “On Some Aspects of Helicopter Flight Dynamics in Steady Turns,” Journal of Guidance, Control and Dynamics, Vol. 21, No. 3, 1998, pp. 383-390. doi:10.2514/2.4270 [9] T. S. Beddoes, “Representation of Airfoil Behaviour,” Vertica, Vol. 7, No. 2, 1983, pp. 183-197. [10] D. A. Peters and N. Haquang, “Dynamic Inflow for Practical Applications,” Journal of the American Helicopter Society, Vol. 33, No. 4, 1988, pp. 64-68. doi:10.4050/JAHS.33.64 [11] R. Celi, “Hingeless Rotor Dynamics in Coordinated Turns,” Journal of the American Helicopter Society, Vol. 36, No. 4, 1991, pp. 39-47. doi:10.4050/JAHS.36.39 [12] D. L. Carrol, “Chemical Laser Modeling with Genetic Algorithms,” AIAA Journal, Vol. 34, No. 2, 1996, pp. 338-346. doi:10.2514/3.13069 [13] D. L. Carroll, “Genetic Algorithms and Optimizing Chemical Oxygen-Iodine Lasers,” Developments in Theoretical and Applied Mechanics, Vol. 18, 1996, pp. 411-424. [14] R. Fantinutto, G. Guglieri and F. Quagliotti, “Flight Control System Design and Optimisation with a Genetic Algorithm,” Aerospace Science and Technology, Vol. 9, No. 1, 2005, pp. 73-80. doi:10.1016/j.ast.2004.09.003 [15] G. Guglieri, B. Pralio and F. Quagliotti, “Flight Control System Design for a Micro Aerial Vehicle,” Aircraft Engineering and Aerospace Technology, Vol. 78, No. 2, 2006, pp. 87-97. doi:10.1108/17488840610653397